
import numpy as np # mathematical manipulations
import pandas as pd # data manipulations
from pandas import ExcelWriter # for writing excel files with multiple sheets

# for visualisation
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
sns.set_style('darkgrid')

# pre-processing of data
from sklearn.preprocessing import LabelEncoder # for one-hot encoding
from scipy.stats import zscore # for standardising the scale

from sklearn.model_selection import train_test_split # splitting into train and test sets

# ML model libraries
from sklearn.linear_model import LinearRegression
from sklearn.cluster import KMeans,AgglomerativeClustering # clustering

# various model performance measures
from sklearn.metrics import silhouette_samples,silhouette_score
from scipy.cluster.hierarchy import cophenet, dendrogram ,linkage, fcluster

from scipy.spatial.distance import pdist # pairwise distribution
from scipy.spatial.distance import cdist # computing distance between each pair

# ignore warnings
import warnings
warnings.filterwarnings("ignore")


# importing data to a data frame from json
mpg_json = pd.read_json('Part1 - Car-Attributes.json')


# checking top five rows to see if data is imported
mpg_json.head()


# analysing shape of the dataframe
mpg_json.shape

(398, 8)


# importing data to a data frame from json
car_names = pd.read_csv('Part1 - Car name.csv')


# checking top five rows to see if data is imported
car_names.head()


# analysing shape of the dataframe
car_names.shape

(398, 1)


# combine by join()
mpg_combined = mpg_json.join(car_names)


# combine by concat()
# mpg_combined = pd.concat([mpg_json,car_names],axis=1)


# checking top five rows to see if data is joined
mpg_combined.head()


# analysing shape of the dataframe
mpg_combined.shape

(398, 9)


# export combined dataframe to csv
mpg_combined.to_csv('mpg.csv',index=False) # didn't save the index value


# export combined dataframe to excel sheet
mpg_combined.to_excel('mpg.xlsx',sheet_name='Miles Per Gallon',index=False)


# export combined dataframe to excel sheet
# required if we want to write multiple sheets to single excel workbook
# writer = ExcelWriter('mpg.xlsx') # initialising the writer object
# mpg_combined.to_excel(writer,sheet_name='Miles Per Gallon',index=False)


# export combined dataframe to json
mpg_combined.to_json('mpg.json',orient='table',index=False)


# importing data to a data frame from csv
mpg = pd.read_csv('mpg.csv')


# checking top five rows to see if data is imported
mpg.head()


# checking shape of the data imported to verify our import/export
mpg.shape

(398, 9)


# importing data to a data frame from excel
# mpg = pd.read_excel('mpg.xlsx')
# mpg.head()


# importing data to a data frame from json
# mpg = pd.read_json('mpg.json',orient='table')
# mpg.head()


# checking null values
mpg.isnull().sum()

mpg         0
cyl         0
disp        0
hp          0
wt          0
acc         0
yr          0
origin      0
car_name    0
dtype: int64


# seeing shape and data types of various features
mpg.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 398 entries, 0 to 397
Data columns (total 9 columns):
 #   Column    Non-Null Count  Dtype  
---  ------    --------------  -----  
 0   mpg       398 non-null    float64
 1   cyl       398 non-null    int64  
 2   disp      398 non-null    float64
 3   hp        398 non-null    object 
 4   wt        398 non-null    int64  
 5   acc       398 non-null    float64
 6   yr        398 non-null    int64  
 7   origin    398 non-null    int64  
 8   car_name  398 non-null    object 
dtypes: float64(3), int64(4), object(2)
memory usage: 28.1+ KB


# checking for bad values in hp column
isHpDigit = mpg['hp'].str.isdigit()
mpg[isHpDigit == False] # seeing the bad value


# replacing ? with NaN
mpg['hp'].replace(to_replace='?',value=np.nan,inplace=True)
mpg['hp'] = mpg['hp'].astype('float64')  # change data type to float
mpg[isHpDigit == False] # seeing if the values are replaced


# replacing NaN with median
mpg['hp'].fillna(value=mpg['hp'].median(),inplace=True)


# replacing the categorical var with actual values
mpg['origin'].replace({1: 'america', 2: 'europe', 3: 'asia'},inplace=True)
mpg['origin'] = mpg['origin'].astype('category')  # change data type to category


# only numeric features are considered for clustering, hence we extract those by
# dropping non-numeric features froma copy of data frame
mpg_copy = mpg.copy() # creating copy of df to keep original data intact


def separate_numeric_and_non_numeric_features(df, num_col, cat_col):
    for col in df.columns:
        if str(df[col].dtypes) == 'category' or df[col].dtypes == 'object':
            cat_col.append(col)
        else:
            num_col.append(col)


numeric_features = [] # initialising list to hold numeric features
non_numeric_features = [] # initialising list to hold non-numeric features
# calling the function to separate out features based on data type
separate_numeric_and_non_numeric_features(mpg,numeric_features,non_numeric_features)


# dropping non-numeric features
mpg_copy.drop(non_numeric_features,axis=1,inplace=True)


# 5-point summary and other statistical features of all features
mpg_copy.describe(include='all').transpose()


def pie_chart_and_count_plot(df,col_name):
    # initialising subplots
    figure, ax = plt.subplots(nrows=1, ncols=2)

    # setting figure parameters
    figure.set_figheight(7)
    figure.set_figwidth(15)
    
    #setting axes titles
    ax[0].set_title(f'Pie Chart of {col_name.upper()}')
    ax[1].set_title(f'Count plot of {col_name.upper()}')

    # plotting the distribution
    df[col_name].value_counts().plot.pie(ax=ax[0],autopct='%1.1f%%')
    sns.countplot(x=col_name,data=df,ax=ax[1])

    plt.show()


# seeing various values for categorical variables
for col in non_numeric_features:
    if col != 'car_name':
        print(f'Values for {col.upper()}')
        print(mpg[col].value_counts())
        print()
        pie_chart_and_count_plot(mpg,col) # call to plotting function
        print('*'*40)

Values for ORIGIN
america    249
asia        79
europe      70
Name: origin, dtype: int64

****************************************


def calculateLowerAndUpperFence(df,col_name):
    Q1 = np.percentile(a=df[col_name],q=25) # 25th percentile
    Q3 = np.percentile(a=df[col_name],q=75) # 75th percentile
    IQR = Q3 - Q1 # inter-quartile-range
    cut_off = 1.5 * IQR
    # limit for outliers
    lower_fence = Q1 - cut_off
    upper_fence = Q3 + cut_off
    return lower_fence,upper_fence


def number_of_outliers(df,col_name,lower_fence,upper_fence):
    outliers = np.where((df[col_name] < lower_fence) | (df[col_name] > upper_fence))
    number_of_outliers = len(outliers[0])
    return number_of_outliers


def plotHistogramAndBoxPlotWithOutliers(df,col_name, outlier_col):
    # initialising subplots
    figure, ax = plt.subplots(nrows=1, ncols=2)

    # setting figure parameters
    figure.set_figheight(5)
    figure.set_figwidth(15)

    #setting axes titles
    ax[0].set_title(f'Distribution plot of {col_name.upper()}')
    ax[1].set_title(f'Box plot of {col_name.upper()}')

    # plotting the distribution
    sns.histplot(data=df[col_name],ax=ax[0],kde=True,color='g')
    sns.boxplot(x=df[col_name],ax=ax[1])

    plt.show()

    # figuring upper and lower limits
    lower_fence,upper_fence = calculateLowerAndUpperFence(df,col_name)

    # logical selection of outliers
    no_of_outliers = number_of_outliers(df,col_name,lower_fence,upper_fence)
    if no_of_outliers > 0:
        outlier_col.append(col_name)
        print(f'The number of outliers for {col_name} are {no_of_outliers}')


outlier_cols = [] # initialising list to hold outlier columns
for col in numeric_features:
    plotHistogramAndBoxPlotWithOutliers(mpg,col,outlier_cols) # call to appropriate function
    print('*'*40)

The number of outliers for mpg are 1
****************************************

****************************************

****************************************

The number of outliers for hp are 11
****************************************

****************************************

The number of outliers for acc are 7
****************************************

****************************************


# pairwise scatter plots and the distributions
sns.pairplot(data=mpg_copy,diag_kind='kde') 
plt.show()


corr = mpg_copy.corr() # finding the correlation matrix
corr


# visualisation of correlation matrix
plt.figure(figsize=(10,8))
sns.heatmap(corr, annot=True)
plt.show()


def pull_outliers_to_quartile_limits(df,outlier_cols):
    for col_name in outlier_cols:
        lower_fence,upper_fence = calculateLowerAndUpperFence(df,col_name)
        print(f'The number of outliers for {col_name} before imputing are {number_of_outliers(df,col_name,lower_fence,upper_fence)}')

        col_index = df.columns.get_loc(col_name)
        # imputing values lower than lower fence equal to lower fence
        for row_index in np.where(df[col_name] < lower_fence)[0]:
            df.iloc[[row_index],[col_index]] = lower_fence

        # imputing values higher than upper fence equal to upper fence
        for row_index in np.where(df[col_name] > upper_fence)[0]:
            df.iloc[[row_index],[col_index]] = upper_fence
        
        print(f'The number of outliers for {col_name} after imputing are {number_of_outliers(df,col_name,lower_fence,upper_fence)}')
        print()


pull_outliers_to_quartile_limits(mpg_copy,outlier_cols)  # call to appropriate function

The number of outliers for mpg before imputing are 1
The number of outliers for mpg after imputing are 0

The number of outliers for hp before imputing are 11
The number of outliers for hp after imputing are 0

The number of outliers for acc before imputing are 7
The number of outliers for acc after imputing are 0


# standardising the data as the distance measures are very sensitive to scale
mpg_scaled = mpg_copy.apply(zscore)


clusters = range(2,10) # number of clusters to try
meanDistortions = [] 
silhouette_scores = []

for k in clusters:
    model=KMeans(n_clusters=k) # model object
    model.fit(mpg_scaled) # fit the model
    prediction = model.predict(mpg_scaled) # making predictions
    # taking minimum distortions
    meanDistortions.append(sum(np.min(cdist(mpg_scaled, model.cluster_centers_, 'euclidean'), axis=1)) / mpg_scaled.shape[0])
    score = silhouette_score(mpg_scaled,model.labels_) # calculating silhouette score
    silhouette_scores.append(score)


# plotting the average distortion
plt.figure(figsize=(10,8))
plt.plot(clusters, meanDistortions, 'bx-')
plt.xlabel('k')
plt.ylabel('Average distortion')
plt.title('Selecting k with the Elbow Method')
plt.show()


# plotting the silhouette scores
plt.figure(figsize=(10,8))
plt.plot(clusters, silhouette_scores, 'bx-')
plt.xlabel('k')
plt.ylabel('Silhouette Score')
plt.title('Selecting k with the Elbow Method')
plt.show()


model = KMeans(n_clusters=4) # model object
model.fit(mpg_scaled) # fit the model
prediction = model.predict(mpg_scaled) # making predictions

# append to the data frame
mpg_copy['GROUP'] = prediction # for seeing difference, original data frame
mpg_scaled['GROUP'] = prediction # for scale difference for box plot
mpg['GROUP_K4'] = prediction # to original data set for running Linear Regression
mpg_scaled.head()


mpg_copy.groupby(by=['GROUP']).describe()


mpg_copy.groupby(by=['GROUP']).mean()


mpg_scaled.boxplot(by='GROUP',layout=(2,4),figsize=(15,10))
plt.show()


model = KMeans(n_clusters=6) # model object
model.fit(mpg_scaled) # fit the model
prediction = model.predict(mpg_scaled) # making predictions

# append to the data frame
mpg_copy['GROUP'] = prediction # for seeing difference, original data frame
mpg_scaled['GROUP'] = prediction # for scale difference for box plot
mpg_scaled.head()


mpg_copy.groupby(by=['GROUP']).describe()


mpg_copy.groupby(by=['GROUP']).mean()


mpg_scaled.boxplot(by='GROUP',layout=(2,4),figsize=(15,10))
plt.show()


# building model object with already decided clusters
model = AgglomerativeClustering(n_clusters=4,affinity='euclidean',linkage='average')

# fit the model
model.fit(mpg_scaled)

# cluster labels for each point
mpg_copy['GROUP'] = model.labels_
mpg_scaled['GROUP'] = model.labels_
mpg_scaled.head()


linkage_methods = ['average', 'complete', 'ward']

for link in linkage_methods:
    z = linkage(y= mpg_scaled, method=link,metric='euclidean')
    cop_corr, cop_dist = cophenet(z, pdist(mpg_scaled))
    print(f'Cophenetic Correlation Coeff for {link} linkage is {cop_corr}') # correlation coeff
    print()
    
    # plotting the dendrogram
    plt.figure(figsize=(20, 20))
    plt.title('Agglomerative Hierarchical Clustering Dendogram')
    plt.xlabel('Sample index')
    plt.ylabel('Distance')
    dendrogram(z)
    plt.tight_layout()
    plt.show()
    
    print('*'*40)

Cophenetic Correlation Coeff for average linkage is 0.770939946260354

****************************************
Cophenetic Correlation Coeff for complete linkage is 0.7986245990773655

****************************************
Cophenetic Correlation Coeff for ward linkage is 0.7564003468959983

****************************************


z = linkage(y= mpg_scaled, method='average',metric='euclidean')

# plotting the dendrogram
plt.figure(figsize=(7, 7))
plt.title('Agglomerative Hierarchical Clustering Dendogram')
plt.xlabel('Sample index')
plt.ylabel('Distance')
dendrogram(z, truncate_mode='lastp',p=4)
plt.tight_layout()
plt.show()


#adding the clusters formed to original data set
clusters = fcluster(z, 4, criterion='maxclust')
mpg_scaled['GROUP'] = clusters
mpg_copy['GROUP'] = clusters
mpg['GROUP_H4'] = clusters


mpg_copy.groupby(by=['GROUP']).describe()


mpg_copy.groupby(by=['GROUP']).mean()


mpg_scaled.boxplot(by='GROUP',layout=(2,4),figsize=(15,10))
plt.show()


# making the clusters as categorical
mpg['GROUP_K4'] = mpg['GROUP_K4'].astype('category')
mpg['GROUP_H4'] = mpg['GROUP_H4'].astype('category')


# dummy variables - One_hot Encoding
mpg = pd.get_dummies(data= mpg, columns=['origin','GROUP_K4','GROUP_H4'])
mpg.head()
# splits n categorical variable values into n columns, but we can use n-1 columns, because we can know the state of all


# drop the dependant variable and also the nth dummy variable

# independant variables without clusters
X = mpg.drop(columns=['mpg','origin_europe', 'car_name','GROUP_K4_0','GROUP_K4_1','GROUP_K4_2','GROUP_K4_3','GROUP_H4_1','GROUP_H4_2','GROUP_H4_3','GROUP_H4_4'],axis=1)

# dependant variable
y = mpg[['mpg']]


# split data into training and test data in 70:30 ratio
X_train,X_test,y_train,y_test = train_test_split(X, y, test_size=0.30,random_state=1)


regression_model = LinearRegression()
regression_model.fit(X_train,y_train)  # fit the linear regression model

LinearRegression()


# coefficients of various independant variables
for indx, col_name in enumerate(X_train.columns):
    print(f'The Regression coefficient for {col_name} is {regression_model.coef_[0][indx]}')
    
# also the variables don't only influence mpg they influence each other too

The Regression coefficient for cyl is -0.39480796616482017
The Regression coefficient for disp is 0.028945510765487167
The Regression coefficient for hp is -0.02175220772354671
The Regression coefficient for wt is -0.007352032065147344
The Regression coefficient for acc is 0.06191936600761839
The Regression coefficient for yr is 0.8369338917644991
The Regression coefficient for origin_america is -3.0012830009185163
The Regression coefficient for origin_asia is -0.6060179643247368


intercept = regression_model.intercept_[0]
print(f'The intercept is {intercept}')

The intercept is -18.283451116372103


#R^2 for in-sample/training
in_sample_score_without_clusters = regression_model.score(X_train,y_train)
print(f'In-Sample score without clusters is {in_sample_score_without_clusters}')

In-Sample score without clusters is 0.8141025501610559


#R^2 for out-sample/testing
out_sample_score_without_clusters = regression_model.score(X_test,y_test)
print(f'Out-Sample score without clusters is {out_sample_score_without_clusters}')

Out-Sample score without clusters is 0.8433135132808833


# drop the dependant variable and also the nth dummy variable

# independant variables with K-Means clusters
X = mpg.drop(columns=['mpg','origin_europe', 'car_name','GROUP_K4_3','GROUP_H4_1','GROUP_H4_2','GROUP_H4_3','GROUP_H4_4'],axis=1)

# dependant variable
y = mpg[['mpg']]


# split data into training and test data in 70:30 ratio
X_train,X_test,y_train,y_test = train_test_split(X, y, test_size=0.30,random_state=1)


regression_model = LinearRegression()
regression_model.fit(X_train,y_train)  # fit the linear regression model

LinearRegression()


# coefficients of various independant variables
for indx, col_name in enumerate(X_train.columns):
    print(f'The Regression coefficient for {col_name} is {regression_model.coef_[0][indx]}')
    
# also the variables don't only influence mpg they influence each other too

The Regression coefficient for cyl is 1.634501749036185
The Regression coefficient for disp is 0.015291016964198132
The Regression coefficient for hp is -0.039226308573234996
The Regression coefficient for wt is -0.0056692688686669716
The Regression coefficient for acc is 0.13917911671698766
The Regression coefficient for yr is 0.3586288401372979
The Regression coefficient for origin_america is -3.0663035121939606
The Regression coefficient for origin_asia is -0.7848913538076852
The Regression coefficient for GROUP_K4_0 is 10.336805112233936
The Regression coefficient for GROUP_K4_1 is 0.6296220121495208
The Regression coefficient for GROUP_K4_2 is 4.557441506950371


intercept = regression_model.intercept_[0]
print(f'The intercept is {intercept}')

The intercept is 1.0826278914466307


#R^2 for in-sample/training
in_sample_score_with_k_means_clusters = regression_model.score(X_train,y_train)
print(f'In-Sample score with K-Means clusters is {in_sample_score_with_k_means_clusters}')

In-Sample score with K-Means clusters is 0.8771119915206057


#R^2 for out-sample/testing
out_sample_score_with_k_means_clusters = regression_model.score(X_test,y_test)
print(f'Out-Sample score with K-Means clusters is {out_sample_score_with_k_means_clusters}')

Out-Sample score with K-Means clusters is 0.8794030418447716


model_lists = []
model_lists.append(['Linear Regression on Original Data set',in_sample_score_without_clusters*100 ,out_sample_score_without_clusters*100])
model_lists.append(['Linear Regression with Clustering', in_sample_score_with_k_means_clusters*100, out_sample_score_with_k_means_clusters*100])
mdl_df = pd.DataFrame(model_lists, columns = ['Model','r^2 on Train Data','r^2 on Test Data'])
mdl_df


# majority of the libraries are imported already into the notebook from above
from sklearn.linear_model import LogisticRegression # for classification

# model performance measures
from sklearn.metrics import confusion_matrix


# importing data to a data frame from xlsx
company = pd.read_excel('Part2 - Company.xlsx')

# verifying the import by seeing top 5 records
company.head()


# analysing shape of data set
company.shape

(61, 5)


# seeing datatypes
company.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 61 entries, 0 to 60
Data columns (total 5 columns):
 #   Column   Non-Null Count  Dtype 
---  ------   --------------  ----- 
 0   A        61 non-null     int64 
 1   B        61 non-null     int64 
 2   C        61 non-null     int64 
 3   D        61 non-null     int64 
 4   Quality  43 non-null     object
dtypes: int64(4), object(1)
memory usage: 2.5+ KB


company.isnull().sum()

A           0
B           0
C           0
D           0
Quality    18
dtype: int64


# analysing unique values in the output
company['Quality'].unique()

array(['Quality A', 'Quality B', nan], dtype=object)


# visual guess of the number clusters
sns.pairplot(data=company, diag_kind='kde')
plt.show()


# seeing various values for categorical variables
print(f'Values for QUALITY')
print(company['Quality'].value_counts())
print()
pie_chart_and_count_plot(company,'Quality') # call to plotting function

Values for QUALITY
Quality A    26
Quality B    17
Name: Quality, dtype: int64


outlier_cols = [] # initialising list to hold outlier columns
for col in company.columns:
    if col != 'Quality':
        plotHistogramAndBoxPlotWithOutliers(company,col,outlier_cols) # call to appropriate function
        print('*'*40)

****************************************

****************************************

****************************************

****************************************


# copying original dataset for manipulation
company_copy = company.copy()


# dropping the target variable
company_copy.drop(['Quality'],axis=1,inplace=True)


# standardising the scale
company_scaled = company_copy.apply(zscore)


model = KMeans(n_clusters=2)
model.fit(company_scaled) # fit the model
prediction = model.predict(company_scaled) # making predictions

# append to the data frame
company['GROUP_K2'] = prediction
company.head()


# renaming the cluster labels
company['GROUP_K2'].replace({1: 'Quality A', 0: 'Quality B'},inplace=True)


# dropping null values
company.dropna(inplace=True)


def plot_confusion_matrix(actual,pred,actual_labels,pred_labels): # function to plot confusion matrix
    cm = confusion_matrix(actual,pred,labels=actual_labels) # getting the confusion matrix
    cm_df = pd.DataFrame(cm, index=[i for i in actual_labels],columns=[i for i in pred_labels])

    plt.figure(figsize=(7,5))

    sns.heatmap(cm_df, annot=True,fmt='g')
    plt.show()


actual_labels = ['Quality B','Quality A']
pred_labels = ['Predicted '+label for label in actual_labels]
plot_confusion_matrix(company['Quality'],company['GROUP_K2'],actual_labels,pred_labels) # plotting confusion matrix


# majority of the libraries are imported already into the notebook from above
from sklearn.decomposition import PCA # Principal Component Analysis

# ML models
from sklearn.svm import SVC # Support Vector Classifier

# model performance
from sklearn.metrics import classification_report,accuracy_score


# reading data from csv to a dataframe
vehicle = pd.read_csv('Part3 - vehicle.csv')

# verifying data import by seeing top 5 rows
vehicle.head()


# analysing shape of dataframe
vehicle.shape

(846, 19)


# analysing the data types of dataframe
vehicle.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 846 entries, 0 to 845
Data columns (total 19 columns):
 #   Column                       Non-Null Count  Dtype  
---  ------                       --------------  -----  
 0   compactness                  846 non-null    int64  
 1   circularity                  841 non-null    float64
 2   distance_circularity         842 non-null    float64
 3   radius_ratio                 840 non-null    float64
 4   pr.axis_aspect_ratio         844 non-null    float64
 5   max.length_aspect_ratio      846 non-null    int64  
 6   scatter_ratio                845 non-null    float64
 7   elongatedness                845 non-null    float64
 8   pr.axis_rectangularity       843 non-null    float64
 9   max.length_rectangularity    846 non-null    int64  
 10  scaled_variance              843 non-null    float64
 11  scaled_variance.1            844 non-null    float64
 12  scaled_radius_of_gyration    844 non-null    float64
 13  scaled_radius_of_gyration.1  842 non-null    float64
 14  skewness_about               840 non-null    float64
 15  skewness_about.1             845 non-null    float64
 16  skewness_about.2             845 non-null    float64
 17  hollows_ratio                846 non-null    int64  
 18  class                        846 non-null    object 
dtypes: float64(14), int64(4), object(1)
memory usage: 125.7+ KB


# checking for null values
vehicle.isnull().sum()

compactness                    0
circularity                    5
distance_circularity           4
radius_ratio                   6
pr.axis_aspect_ratio           2
max.length_aspect_ratio        0
scatter_ratio                  1
elongatedness                  1
pr.axis_rectangularity         3
max.length_rectangularity      0
scaled_variance                3
scaled_variance.1              2
scaled_radius_of_gyration      2
scaled_radius_of_gyration.1    4
skewness_about                 6
skewness_about.1               1
skewness_about.2               1
hollows_ratio                  0
class                          0
dtype: int64


# copying dataframe for manipulation, to keep original dataset intact
vehicle_copy = vehicle.copy()


# dropping null values
vehicle_copy.dropna(inplace=True)


# seeing various values for categorical variables
print(f'Values for CLASS')
print(vehicle_copy['class'].value_counts())
print()
pie_chart_and_count_plot(vehicle_copy,'class') # call to plotting function

Values for CLASS
car    413
bus    205
van    195
Name: class, dtype: int64


# seeing the distribution numeric features
outlier_cols = [] # initialising list to hold outlier columns
for col in vehicle_copy.columns:
    if col != 'class':
        plotHistogramAndBoxPlotWithOutliers(vehicle_copy,col,outlier_cols) # call to appropriate function
        print('*'*50)

**************************************************

**************************************************

**************************************************

The number of outliers for radius_ratio are 3
**************************************************

The number of outliers for pr.axis_aspect_ratio are 8
**************************************************

The number of outliers for max.length_aspect_ratio are 13
**************************************************

**************************************************

**************************************************

**************************************************

**************************************************


# 5-point summary and other statistical features of all features
vehicle_copy.describe(include='all').transpose()


# pairwise scatter plots and the distributions
plt.figure(figsize=(25,25))
sns.pairplot(data=vehicle_copy,diag_kind='kde',hue='class') 
plt.show()

<Figure size 1800x1800 with 0 Axes>


corr = vehicle_copy.corr()  # finding the correlation matrix
corr


# visualisation of correlation matrix
plt.figure(figsize=(20,18))
sns.heatmap(corr, annot=True)
plt.show()


# imputing values to quartiles
pull_outliers_to_quartile_limits(vehicle_copy,outlier_cols)  # call to appropriate function

The number of outliers for radius_ratio before imputing are 3
The number of outliers for radius_ratio after imputing are 0

The number of outliers for pr.axis_aspect_ratio before imputing are 8
The number of outliers for pr.axis_aspect_ratio after imputing are 0

The number of outliers for max.length_aspect_ratio before imputing are 13
The number of outliers for max.length_aspect_ratio after imputing are 0

The number of outliers for scaled_variance before imputing are 1
The number of outliers for scaled_variance after imputing are 0

The number of outliers for scaled_variance.1 before imputing are 2
The number of outliers for scaled_variance.1 after imputing are 0

The number of outliers for scaled_radius_of_gyration.1 before imputing are 15
The number of outliers for scaled_radius_of_gyration.1 after imputing are 0

The number of outliers for skewness_about before imputing are 12
The number of outliers for skewness_about after imputing are 0

The number of outliers for skewness_about.1 before imputing are 3
The number of outliers for skewness_about.1 after imputing are 0


# splitting independant and dependant variables

# independant variables
X = vehicle_copy.drop(['class'],axis=1)

# dependant variable
y = vehicle_copy[['class']]


# standardising the scale
XScaled = X.apply(zscore)


# split data into training and test data in 70:30 ratio
X_train,X_test,y_train,y_test = train_test_split(XScaled, y, test_size=0.30,random_state=1)


# SVM with linear kernel
svc_model_linear = SVC(kernel='linear',C=1,gamma=0.1)


# fit the model on train data
svc_model_linear.fit(X_train,y_train)

SVC(C=1, gamma=0.1, kernel='linear')


# predict on test data
y_pred = svc_model_linear.predict(X_test)


# out-sample/test accuracy
acc_score_all_attr = accuracy_score(y_test,y_pred)
print(f'The accuracy of SVM model with all attributes is {acc_score_all_attr}')

The accuracy of SVM model with all attributes is 0.9549180327868853


print('Classification Report of SVM model with all attributes::')
print()
print(classification_report(y_test,y_pred))

Classification Report of SVM model with all attributes::

              precision    recall  f1-score   support

         bus       0.89      0.96      0.93        53
         car       0.98      0.94      0.96       139
         van       0.94      0.98      0.96        52

    accuracy                           0.95       244
   macro avg       0.94      0.96      0.95       244
weighted avg       0.96      0.95      0.96       244


actual_labels = ['bus','car','van']
pred_labels = ['Predicted '+label for label in actual_labels]
plot_confusion_matrix(y_test,y_pred,actual_labels,pred_labels) # plotting confusion matrix


# creating pca model
pca = PCA(n_components=18) # using all independant variables
pca.fit(XScaled)

PCA(n_components=18)


# EIGEN VALUES
print(pca.explained_variance_)

[9.75592419e+00 3.26593719e+00 1.20041435e+00 1.17739558e+00
 8.54137636e-01 7.49103421e-01 3.46961731e-01 2.22403000e-01
 1.32208401e-01 9.75160321e-02 6.02445504e-02 5.48653930e-02
 3.99381171e-02 2.67746874e-02 1.87215832e-02 1.29036168e-02
 6.35471760e-03 3.63296016e-04]


# EIGEN VECTORS
print(pca.components_)

[[ 2.71463705e-01  2.88738771e-01  3.02090034e-01  2.68754016e-01
   9.94008356e-02  1.87354463e-01  3.10591274e-01 -3.08226743e-01
   3.07430319e-01  2.77583559e-01  3.01196840e-01  3.07688844e-01
   2.63196430e-01 -4.69296924e-02  3.95256577e-02  6.31253277e-02
   3.67354264e-02  8.75674421e-02]
 [-9.10920177e-02  1.39761567e-01 -4.95406279e-02 -1.87222724e-01
  -2.31351754e-01 -9.18135391e-02  7.01377883e-02 -7.98775531e-03
   8.37352473e-02  1.24097679e-01  7.16231923e-02  7.50976087e-02
   2.11241169e-01  5.01893835e-01 -3.97627872e-02 -9.28734091e-02
  -5.13437950e-01 -5.14003078e-01]
 [-1.13518788e-01  1.61271260e-01 -1.12460068e-01  1.75104575e-01
   5.32144075e-01  9.46421926e-02 -1.08663001e-01  4.64332067e-02
  -1.26742878e-01  1.42355297e-01 -5.23578754e-02 -1.15323821e-01
   1.85744400e-01  6.29440753e-02  2.58174083e-03 -7.21169462e-01
   1.78656433e-02 -2.13104973e-02]
 [ 1.61459674e-01  1.24375623e-01  1.14996857e-02 -2.28487196e-01
  -3.90919587e-01  1.37160863e-01 -5.35688885e-02  9.56749083e-02
  -3.32107518e-02  1.64461213e-01 -1.38267336e-01 -5.07068855e-02
   1.10600844e-01 -1.45347452e-01  7.58285227e-01 -2.37024812e-01
   5.18016408e-02  7.35508175e-02]
 [ 1.59995907e-01 -1.00215010e-01 -1.05962555e-01  1.18206374e-01
   1.12277495e-01 -7.73469956e-01  1.06394550e-01 -1.11922995e-01
   9.44104098e-02 -2.54058936e-01  1.47761210e-01  1.46714903e-01
   2.14381365e-02  9.78170120e-02  3.08476702e-01 -1.20790793e-01
   2.32166920e-01 -1.18124715e-01]
 [-1.38473081e-01 -5.52181597e-02 -1.39187548e-02  2.10956881e-01
   4.89317912e-01  1.84582188e-01 -7.14086395e-02  6.79349147e-02
  -7.67198073e-02 -6.40884370e-02  4.78966627e-03 -8.90917406e-02
   4.44502700e-03  2.35482704e-01  5.34809916e-01  4.99808802e-01
  -1.80170911e-01 -9.02509488e-02]
 [ 3.20481648e-01 -3.80211692e-01  8.58383002e-02  1.60032901e-01
  -6.22650373e-02  3.86240819e-01  7.77754023e-02 -6.82465148e-02
   8.00142574e-02 -3.39840789e-01  1.82251522e-01  7.21402344e-02
  -4.64795917e-01  1.53398647e-01  8.50456518e-02 -3.32078258e-01
  -1.28177987e-01 -1.48888355e-01]
 [-7.16051661e-01 -1.06050098e-01  3.42861566e-01  4.63860799e-02
  -9.96062737e-02 -1.03212133e-02  1.08231140e-01 -2.44534468e-01
   6.16526398e-02 -2.76236503e-01  9.96158973e-02  5.61379937e-02
   7.45736860e-02 -3.24402019e-01  1.45202924e-01 -1.53246404e-01
  -1.58696382e-01 -3.23326474e-02]
 [-3.36586613e-01 -4.31365271e-02 -2.62780864e-01  6.79058800e-02
  -2.65239445e-01  1.98608545e-01 -1.65442746e-02 -9.67259241e-02
  -7.73806603e-02  2.47317072e-02  3.97319589e-01 -2.90754562e-02
   8.36756113e-02  4.92641545e-01  2.98251217e-02 -7.91715438e-03
   4.81450109e-01  2.09774590e-01]
 [ 2.59534014e-01 -6.16097896e-02  1.55525279e-01  2.33762192e-01
  -1.64497541e-01  6.13035393e-02 -2.22146316e-01  2.11644276e-01
  -2.57699580e-01 -3.92504200e-01  2.46712435e-01 -2.05472643e-01
   6.05373415e-01 -1.14437841e-01 -9.55822546e-02  4.22823874e-02
  -2.78952542e-02 -1.01114037e-01]
 [-5.77831369e-03 -5.75556319e-02 -7.30533448e-01 -2.97329800e-02
   2.85481381e-02  2.07533025e-01  1.83375529e-01 -4.74359907e-02
   2.69316365e-01 -2.81549422e-01 -1.01667622e-01  2.37245525e-01
   2.78489035e-01 -2.39499964e-01  3.89725737e-03  2.38881210e-02
  -1.53942235e-01  6.85969092e-02]
 [-1.34005379e-01  1.84778855e-01 -2.57013211e-01  5.01885444e-01
  -2.02931641e-01 -5.49127637e-02 -7.08755677e-02  3.35345194e-01
   8.26545229e-03  2.67783817e-01  2.86034704e-01  2.20432866e-02
  -2.94443459e-01 -3.39725152e-01  2.80798609e-02  3.07756824e-02
  -1.74491809e-02 -3.31737340e-01]
 [ 8.41473196e-02  1.29799205e-02 -1.36370829e-01 -2.00642643e-01
   8.22005100e-02  1.46775887e-01 -1.34905317e-02 -5.60800744e-01
  -2.55086890e-01  5.78701753e-02  2.75491708e-02 -1.88670963e-01
  -3.69908591e-03 -2.32522385e-01 -2.67943733e-03  9.56894308e-02
   2.99240676e-01 -5.77367979e-01]
 [-9.55628377e-02 -1.28093099e-01  1.84645149e-01 -3.62962681e-01
   2.07234941e-01  1.55150508e-01  2.85763588e-02  5.15325982e-01
   2.93612771e-01 -3.08002214e-02  8.50045503e-02  2.74256968e-01
   1.02614442e-01 -4.90950587e-02 -1.78976773e-02  6.56388192e-03
   4.10493342e-01 -3.51881616e-01]
 [ 6.07949883e-02 -1.53027967e-01 -1.06190378e-01 -4.48474836e-01
   2.05142523e-01 -9.05333357e-02 -6.57203035e-02  2.33940917e-02
  -1.35909206e-01  1.77536720e-01  6.86341834e-01 -4.50245309e-02
  -5.45748832e-02 -2.01747671e-01  3.43543725e-02 -1.43700193e-02
  -2.96409854e-01  2.22092708e-01]
 [-7.80012945e-03 -7.53873450e-01 -1.08444100e-02  1.87640053e-01
  -5.51191101e-02 -6.09649372e-02 -3.96620088e-02 -6.54273157e-02
   2.00424996e-01  4.93032898e-01 -1.22447413e-01 -1.39418868e-01
   2.50893380e-01 -3.51299429e-02 -1.86719179e-03 -1.15076800e-02
  -1.70978270e-02 -3.72355080e-02]
 [-1.52390693e-02 -1.87292075e-01  6.32187327e-03  5.26282034e-02
  -1.66610673e-02  1.28885750e-02  3.51822736e-01  1.06978578e-01
  -7.07785793e-01  1.21367628e-01 -1.06995339e-01  5.43799277e-01
   5.54569739e-02 -3.75138860e-03  1.73653410e-03 -1.61266008e-03
  -1.59132016e-02 -2.74128252e-03]
 [ 7.71438096e-04  1.44735353e-02 -7.88439419e-03 -2.18305929e-02
   1.59292800e-02 -1.00332679e-02  7.94236607e-01  2.16424549e-01
  -2.07183424e-02 -2.24740669e-02  3.03786954e-02 -5.64062966e-01
   4.35726629e-03 -5.64284927e-03 -3.73033018e-03 -7.38522015e-03
   3.19850663e-02 -8.87946505e-03]]


# PERCENTAGE OF VARIANCE EXPLAINED
print(pca.explained_variance_ratio_)

[5.41329127e-01 1.81217780e-01 6.66076568e-02 6.53304092e-02
 4.73937243e-02 4.15656675e-02 1.92519424e-02 1.23405245e-02
 7.33587685e-03 5.41089367e-03 3.34280272e-03 3.04432822e-03
 2.21605515e-03 1.48565301e-03 1.03880863e-03 7.15985842e-04
 3.52605624e-04 2.01582865e-05]


# percentage of variation explained
plt.figure(figsize=(10,7))
plt.bar(list(range(1,19)),pca.explained_variance_ratio_,alpha=0.5, align='center')
plt.ylabel('Variation explained')
plt.xlabel('Eigen Value')
plt.show()


# cumulative variation explained
plt.figure(figsize=(10,7))
plt.step(list(range(1,19)),np.cumsum(pca.explained_variance_ratio_),where='mid')
plt.ylabel('Cum of variation explained')
plt.xlabel('Eigen Value')
plt.show()


 # use only top 7 features
pca7 = PCA(n_components=7)
pca7.fit(XScaled)

PCA(n_components=7)


Xpca7 = pca7.fit_transform(XScaled) # transform to new variables


# verifying independant or orthogonality of transformed features
sns.pairplot(pd.DataFrame(Xpca7),diag_kind='kde')
plt.show()


# SVM with linear kernel
svc_model_linear = SVC(kernel='linear',C=1,gamma=0.1)


# split data into training and test data in 70:30 ratio
X_train,X_test,y_train,y_test = train_test_split(Xpca7, y, test_size=0.30,random_state=1)


# fit the model on train data
svc_model_linear.fit(X_train,y_train)

SVC(C=1, gamma=0.1, kernel='linear')


# predict on test data
y_pred = svc_model_linear.predict(X_test)


# out-sample/test accuracy
acc_score_few_attr = accuracy_score(y_test,y_pred)
print(f'The accuracy of SVM model with PCA transformed attributes is {acc_score_few_attr}')

The accuracy of SVM model with PCA transformed attributes is 0.8237704918032787


print('Classification Report of SVM model with all attributes::')
print()
print(classification_report(y_test,y_pred))

Classification Report of SVM model with all attributes::

              precision    recall  f1-score   support

         bus       0.67      0.87      0.75        53
         car       0.96      0.79      0.87       139
         van       0.74      0.87      0.80        52

    accuracy                           0.82       244
   macro avg       0.79      0.84      0.81       244
weighted avg       0.85      0.82      0.83       244


actual_labels = ['bus','car','van']
pred_labels = ['Predicted '+label for label in actual_labels]
plot_confusion_matrix(y_test,y_pred,actual_labels,pred_labels) # plotting confusion matrix


model_lists = []
model_lists.append(['Support Vector Classifier without PCA', acc_score_all_attr * 100])
model_lists.append(['Support Vector Classifier with PCA', acc_score_few_attr* 100])
mdl_df = pd.DataFrame(model_lists, columns = ['Model','Accuracy Score of Test Data'])
mdl_df


# almost all the libraries have been imported from above


# importing data into a dataframe from csv
ipl = pd.read_csv('Part4 - batting_bowling_ipl_bat.csv')

# verifying data import
ipl.head()


# analysing the shape
ipl.shape

(180, 7)


# checking data types
ipl.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 180 entries, 0 to 179
Data columns (total 7 columns):
 #   Column  Non-Null Count  Dtype  
---  ------  --------------  -----  
 0   Name    90 non-null     object 
 1   Runs    90 non-null     float64
 2   Ave     90 non-null     float64
 3   SR      90 non-null     float64
 4   Fours   90 non-null     float64
 5   Sixes   90 non-null     float64
 6   HF      90 non-null     float64
dtypes: float64(6), object(1)
memory usage: 10.0+ KB


# checking null values
ipl.isnull().sum()

Name     90
Runs     90
Ave      90
SR       90
Fours    90
Sixes    90
HF       90
dtype: int64


# creating a copy of original dataframe
ipl_copy = ipl.copy()


# dropping all null values
ipl_copy.dropna(inplace=True)


# final shape after dropping nulls
ipl_copy.shape

(90, 7)


def bar_plot_of_top_n_players(y_label,n):
    plt.figure(figsize=(12,6)) # setting figure parameters
    plt.title(f'Top {n} players by {y_label.upper()}')
    
    sns.barplot(x='Name',y=y_label,data=ipl_copy.sort_values(by=y_label,ascending=False).head(n)) # bar plot
    
    plt.tight_layout()
    plt.show()


for col in ipl_copy.columns:
    if col != 'Name':
        bar_plot_of_top_n_players(col,10) # plotting top 10 records
        print()
        print('*'*50)

**************************************************

**************************************************

**************************************************

**************************************************

**************************************************

**************************************************


outlier_cols = []
for col in ipl_copy.columns:
    if col != 'Name':
        plotHistogramAndBoxPlotWithOutliers(ipl_copy,col,outlier_cols) # call to appropriate function
        print()
        print('*'*50)

The number of outliers for Runs are 1

**************************************************

The number of outliers for Ave are 3

**************************************************

The number of outliers for SR are 5

**************************************************

The number of outliers for Fours are 3

**************************************************

The number of outliers for Sixes are 1

**************************************************

The number of outliers for HF are 2

**************************************************


# 5-point summary
ipl_copy.describe(include='all').T


# pairwise scatter plot of numeric features
sns.pairplot(data=ipl_copy.iloc[:,1:],diag_kind='kde')
plt.show()


# correlation matrix
corr = ipl_copy.corr()
corr


# visualisation of correlation matrix
plt.figure(figsize=(12,10))
sns.heatmap(corr, annot=True)
plt.show()


# extracting only numeric features
X = ipl_copy.drop(['Name'],axis=1)


# standardising the scale
XScaled = X.apply(zscore)


# creating pca model
pca = PCA(n_components=6) # using all numeric features
pca.fit(XScaled)

PCA(n_components=6)


# EIGEN VALUES
print(pca.explained_variance_)

[4.30252561 0.83636692 0.41665751 0.32912443 0.16567829 0.01706297]


# EIGEN VECTORS
print(pca.components_)

[[ 0.4582608   0.39797313  0.3253838   0.40574167  0.41733459  0.43237178]
 [ 0.26643209 -0.33111756 -0.69780334  0.47355804 -0.17902455  0.27593225]
 [-0.10977942  0.00550486 -0.45013448 -0.50823538  0.66942589  0.28082541]
 [-0.00520142  0.84736307 -0.43275029 -0.03252305 -0.24878157 -0.17811777]
 [ 0.45840889 -0.10122837 -0.11890348  0.09676885  0.39458014 -0.77486668]
 [ 0.70483594 -0.0606373   0.05624934 -0.58514214 -0.35786211  0.16096217]]


# PERCENTAGE OF VARIANCE EXPLAINED
print(pca.explained_variance_ratio_)

[0.70911996 0.13784566 0.06867133 0.05424458 0.02730624 0.00281223]


# percentage of variation explained
plt.figure(figsize=(10,7))
plt.bar(list(range(1,7)),pca.explained_variance_ratio_,alpha=0.5, align='center')
plt.ylabel('Variation explained')
plt.xlabel('Eigen Value')
plt.show()


# cumulative variation explained
plt.figure(figsize=(10,7))
plt.step(list(range(1,7)),np.cumsum(pca.explained_variance_ratio_),where='mid')
plt.ylabel('Cum of variation explained')
plt.xlabel('Eigen Value')
plt.show()


# use only top 4 features
pca4 = PCA(n_components=4)
pca4.fit(XScaled)

PCA(n_components=4)


Xpca4 = pca4.fit_transform(XScaled) # transform to new variables


# verifying independant or orthogonality of transformed features
sns.pairplot(pd.DataFrame(Xpca4),diag_kind='kde')
plt.show()


clusters = range(2,10) # number of clusters to try
meanDistortions = [] 

for k in clusters:
    model=KMeans(n_clusters=k) # model object
    model.fit(XScaled) # fit the model
    prediction = model.predict(XScaled) # making predictions
    # taking minimum distortions
    meanDistortions.append(sum(np.min(cdist(XScaled, model.cluster_centers_, 'euclidean'), axis=1)) / XScaled.shape[0])


# plotting the average distortion
plt.figure(figsize=(10,5))
plt.plot(clusters, meanDistortions, 'bx-')
plt.xlabel('k')
plt.ylabel('Average distortion')
plt.title('Selecting k with the Elbow Method')
plt.show()


model = KMeans(n_clusters=4) # model object
model.fit(XScaled) # fit the model
prediction = model.predict(XScaled) # making predictions

# append to the data frame
XScaled['GROUP'] = prediction # for seeing difference, original data frame
XScaled['GROUP'] = prediction # for scale difference for box plot
ipl_copy['GROUP'] = prediction # to original data set
ipl_copy.head()


XScaled.groupby(by=['GROUP']).mean()


XScaled.boxplot(by='GROUP',layout=(2,4),figsize=(15,10))
plt.show()


# replacing rank with groups
ipl_copy['GROUP'].replace({0:'Grade A',1:'Grade B',2:'Grade C',3:'Grade D'},inplace=True)


ipl_copy['GROUP'] = ipl_copy['GROUP'].astype('category')


# seeing group A players
ipl_copy[ipl_copy['GROUP'] == 'Grade A']


# seeing group B players
ipl_copy[ipl_copy['GROUP'] == 'Grade B']


# seeing group C players
ipl_copy[ipl_copy['GROUP'] == 'Grade C']


# seeing group D players
ipl_copy[ipl_copy['GROUP'] == 'Grade D']


# all majority of the libraries are imported from above
from sklearn.datasets import load_digits # to use in-built multimedia datasets

# ML model
from sklearn.naive_bayes import GaussianNB # test accuracy between original and dimensionally reduced data

from sklearn.manifold import Isomap # Projection based Dimensionality Reduction Technique


# loading the digits dataset
digits = load_digits()

# analysing shape 
digits.images.shape

(1797, 8, 8)


# visualize the first hundred of digits
import matplotlib.pyplot as plt
fig, axes = plt.subplots(10, 10, figsize=(8, 8),subplot_kw={'xticks':[], 'yticks':[]},gridspec_kw=dict(hspace=0.1, wspace=0.1))

for i,ax in enumerate(axes.flat):
    ax.imshow(digits.images[i], cmap='binary', interpolation='nearest')
    ax.text(0.05, 0.05, str(digits.target[i]),
    transform=ax.transAxes, color='green')


# independant features
X = digits.data

# dependant variable
y = digits.target


# split data into training and test data in 70:30 ratio
X_train,X_test,y_train,y_test = train_test_split(X, y, test_size=0.30,random_state=1)


model = GaussianNB() # form model object
 
model.fit(X_train, y_train) # fit the model on train data

pred = model.predict(X_test) # predict from test data


# calculate accuracy score
acc_score_org = accuracy_score(y_test, pred)
print(f'The accuracy of GuassainNB with original data is {acc_score_org}')

The accuracy of GuassainNB with original data is 0.8314814814814815


labels = pd.Series(y).unique()
plot_confusion_matrix(y_test,pred,labels,labels)


print('The Classification Report for GuassainNB with original data is::')
print(classification_report(y_test,pred))

The Classification Report for GuassainNB with original data is::
              precision    recall  f1-score   support

           0       0.98      0.92      0.95        59
           1       0.62      0.86      0.72        49
           2       0.91      0.65      0.76        49
           3       0.94      0.80      0.86        64
           4       0.92      0.89      0.90        61
           5       0.91      0.87      0.89        47
           6       0.98      1.00      0.99        51
           7       0.79      1.00      0.88        57
           8       0.51      0.74      0.60        46
           9       1.00      0.58      0.73        57

    accuracy                           0.83       540
   macro avg       0.86      0.83      0.83       540
weighted avg       0.87      0.83      0.83       540


# transform the data to two dimensions
iso = Isomap(n_components=2)
iso.fit(digits.data)
data_projected = iso.transform(digits.data)


# split data into training and test data in 70:30 ratio
X_train,X_test,y_train,y_test = train_test_split(data_projected, y, test_size=0.30,random_state=1)


model = GaussianNB() # form model object
 
model.fit(X_train, y_train) # fit the model on train data

pred = model.predict(X_test) # predict from test data


# calculate accuracy score
acc_score_red = accuracy_score(y_test, pred)
print(f'The accuracy of GuassainNB with reduced features is {acc_score_red}')

The accuracy of GuassainNB with reduced features is 0.7222222222222222


labels = pd.Series(y).unique()
plot_confusion_matrix(y_test,pred,labels,labels)


print('The Classification Report for GuassainNB with reduced data is::')
print(classification_report(y_test,pred))

The Classification Report for GuassainNB with reduced data is::
              precision    recall  f1-score   support

           0       1.00      0.97      0.98        59
           1       0.81      0.71      0.76        49
           2       0.50      0.35      0.41        49
           3       0.62      0.83      0.71        64
           4       0.97      0.98      0.98        61
           5       0.54      0.53      0.54        47
           6       0.81      0.86      0.84        51
           7       0.53      0.72      0.61        57
           8       0.69      0.87      0.77        46
           9       0.78      0.32      0.45        57

    accuracy                           0.72       540
   macro avg       0.73      0.71      0.70       540
weighted avg       0.73      0.72      0.71       540


model_lists = []
model_lists.append(['GuassianNB without Dimensionality Reduction', acc_score_org * 100])
model_lists.append(['GuassianNB with Dimensionality Reduction', acc_score_red* 100])
mdl_df = pd.DataFrame(model_lists, columns = ['Model','Accuracy Score of Test Data'])
mdl_df

	car_name
0	chevrolet chevelle malibu
1	buick skylark 320
2	plymouth satellite
3	amc rebel sst
4	ford torino

	mpg	cyl	disp	hp	wt	acc	yr	origin	car_name
0	18.0	8	307.0	130	3504	12.0	70	1	chevrolet chevelle malibu
1	15.0	8	350.0	165	3693	11.5	70	1	buick skylark 320
2	18.0	8	318.0	150	3436	11.0	70	1	plymouth satellite
3	16.0	8	304.0	150	3433	12.0	70	1	amc rebel sst
4	17.0	8	302.0	140	3449	10.5	70	1	ford torino

	mpg	cyl	disp	hp	wt	acc	yr	origin	car_name
0	18.0	8	307.0	130	3504	12.0	70	1	chevrolet chevelle malibu
1	15.0	8	350.0	165	3693	11.5	70	1	buick skylark 320
2	18.0	8	318.0	150	3436	11.0	70	1	plymouth satellite
3	16.0	8	304.0	150	3433	12.0	70	1	amc rebel sst
4	17.0	8	302.0	140	3449	10.5	70	1	ford torino

	mpg	cyl	disp	hp	wt	acc	yr	origin	car_name
32	25.0	4	98.0	?	2046	19.0	71	1	ford pinto
126	21.0	6	200.0	?	2875	17.0	74	1	ford maverick
330	40.9	4	85.0	?	1835	17.3	80	2	renault lecar deluxe
336	23.6	4	140.0	?	2905	14.3	80	1	ford mustang cobra
354	34.5	4	100.0	?	2320	15.8	81	2	renault 18i
374	23.0	4	151.0	?	3035	20.5	82	1	amc concord dl

	mpg	cyl	disp	hp	wt	acc	yr	origin	car_name
32	25.0	4	98.0	NaN	2046	19.0	71	1	ford pinto
126	21.0	6	200.0	NaN	2875	17.0	74	1	ford maverick
330	40.9	4	85.0	NaN	1835	17.3	80	2	renault lecar deluxe
336	23.6	4	140.0	NaN	2905	14.3	80	1	ford mustang cobra
354	34.5	4	100.0	NaN	2320	15.8	81	2	renault 18i
374	23.0	4	151.0	NaN	3035	20.5	82	1	amc concord dl

	count	mean	std	min	25%	50%	75%	max
mpg	398.0	23.514573	7.815984	9.0	17.500	23.0	29.000	46.6
cyl	398.0	5.454774	1.701004	3.0	4.000	4.0	8.000	8.0
disp	398.0	193.425879	104.269838	68.0	104.250	148.5	262.000	455.0
hp	398.0	104.304020	38.222625	46.0	76.000	93.5	125.000	230.0
wt	398.0	2970.424623	846.841774	1613.0	2223.750	2803.5	3608.000	5140.0
acc	398.0	15.568090	2.757689	8.0	13.825	15.5	17.175	24.8
yr	398.0	76.010050	3.697627	70.0	73.000	76.0	79.000	82.0

	mpg	cyl	disp	hp	wt	acc	yr
mpg	1.000000	-0.775396	-0.804203	-0.773453	-0.831741	0.420289	0.579267
cyl	-0.775396	1.000000	0.950721	0.841284	0.896017	-0.505419	-0.348746
disp	-0.804203	0.950721	1.000000	0.895778	0.932824	-0.543684	-0.370164
hp	-0.773453	0.841284	0.895778	1.000000	0.862442	-0.686590	-0.413733
wt	-0.831741	0.896017	0.932824	0.862442	1.000000	-0.417457	-0.306564
acc	0.420289	-0.505419	-0.543684	-0.686590	-0.417457	1.000000	0.288137
yr	0.579267	-0.348746	-0.370164	-0.413733	-0.306564	0.288137	1.000000

	mpg	cyl	disp	hp	wt	acc	yr	GROUP
0	-0.706560	1.498191	1.090604	0.713692	0.630870	-1.320595	-1.627426	3
1	-1.090999	1.498191	1.503514	1.666741	0.854333	-1.506489	-1.627426	3
2	-0.706560	1.498191	1.196232	1.258291	0.550470	-1.692383	-1.627426	3
3	-0.962852	1.498191	1.061796	1.258291	0.546923	-1.320595	-1.627426	3
4	-0.834706	1.498191	1.042591	0.985992	0.565841	-1.878278	-1.627426	3

	mpg								cyl		...	acc		yr
	count	mean	std	min	25%	50%	75%	max	count	mean	...	75%	max	count	mean	std	min	25%	50%	75%	max
GROUP
0	114.0	32.724123	5.013536	22.3	29.575	32.35	36.000	46.25	114.0	4.052632	...	18.000	22.2	114.0	80.078947	1.657083	76.0	79.0	80.0	81.75	82.0
1	90.0	19.621111	2.819216	15.0	18.000	19.00	20.950	28.80	90.0	6.177778	...	17.675	22.2	90.0	76.200000	3.024990	70.0	74.0	76.0	78.00	82.0
2	100.0	25.056000	3.528164	18.0	23.000	25.00	27.000	35.00	100.0	4.010000	...	18.000	22.2	100.0	73.590000	2.278999	70.0	72.0	74.0	75.00	78.0
3	94.0	14.429787	2.143900	9.0	13.000	14.00	15.875	20.20	94.0	8.000000	...	13.650	18.5	94.0	73.468085	2.754086	70.0	71.0	73.0	76.00	79.0

	mpg	cyl	disp	hp	wt	acc	yr
GROUP
1	43.700000	4.000000	91.750000	49.000000	2133.750000	21.900000	80.000000
2	28.846934	4.047170	112.134434	80.063679	2336.377358	16.389623	77.009434
3	14.429787	8.000000	350.042553	160.218085	4157.978723	12.591489	73.468085
4	19.451136	6.193182	226.590909	103.164773	3267.409091	16.407955	76.136364

Hierarchical Clustering	K-Means Clustering
connectivity-based clustering	centroid-based clustering
computation time is more	less computation time
we can select any number of clusters	number of clusters are decided at model building time
uses dendrogram to decide on number of clusters	uses elbow method to decide on number of clusters
increases quadratic	increases linearly
uses agglomerative or divisive algorithms	used Llyod's algorithm
can use any distance and linkage measures	uses Eucledian distance measure

	mpg	cyl	disp	hp	wt	acc	yr	car_name	origin_america	GROUP_K4_3	GROUP_H4_3
0	18.0	8	307.0	130.0	3504	12.0	70	chevrolet chevelle malibu	1	1	1
1	15.0	8	350.0	165.0	3693	11.5	70	buick skylark 320	1	1	1
2	18.0	8	318.0	150.0	3436	11.0	70	plymouth satellite	1	1	1
3	16.0	8	304.0	150.0	3433	12.0	70	amc rebel sst	1	1	1
4	17.0	8	302.0	140.0	3449	10.5	70	ford torino	1	1	1

	Model	r^2 on Train Data	r^2 on Test Data
0	Linear Regression on Original Data set	81.410255	84.331351
1	Linear Regression with Clustering	87.711199	87.940304

	A	B	C	D	Quality
0	47	27	45	108	Quality A
1	174	133	134	166	Quality B
2	159	163	135	131	NaN
3	61	23	3	44	Quality A
4	59	60	9	68	Quality A

	compactness	circularity	distance_circularity	radius_ratio	pr.axis_aspect_ratio	max.length_aspect_ratio	scatter_ratio	elongatedness	pr.axis_rectangularity	max.length_rectangularity	scaled_variance	scaled_variance.1	scaled_radius_of_gyration	scaled_radius_of_gyration.1	skewness_about	skewness_about.1	skewness_about.2	hollows_ratio	class
0	95	48.0	83.0	178.0	72.0	10	162.0	42.0	20.0	159	176.0	379.0	184.0	70.0	6.0	16.0	187.0	197	van
1	91	41.0	84.0	141.0	57.0	9	149.0	45.0	19.0	143	170.0	330.0	158.0	72.0	9.0	14.0	189.0	199	van
2	104	50.0	106.0	209.0	66.0	10	207.0	32.0	23.0	158	223.0	635.0	220.0	73.0	14.0	9.0	188.0	196	car
3	93	41.0	82.0	159.0	63.0	9	144.0	46.0	19.0	143	160.0	309.0	127.0	63.0	6.0	10.0	199.0	207	van
4	85	44.0	70.0	205.0	103.0	52	149.0	45.0	19.0	144	241.0	325.0	188.0	127.0	9.0	11.0	180.0	183	bus

	mpg	cyl	disp	hp	wt	acc	yr
GROUP
0	14.429787	8.000000	350.042553	160.218085	4157.978723	12.591489	73.468085
1	24.633824	4.014706	115.000000	89.058824	2404.779412	15.147059	73.941176
2	31.948649	4.040541	113.770270	79.675676	2290.351351	15.095946	79.891892
3	25.953125	4.000000	99.765625	70.421875	2177.281250	19.212500	72.843750
4	19.517978	6.179775	226.101124	102.870787	3270.359551	16.449438	76.168539
5	34.028049	4.121951	110.365854	70.414634	2381.292683	19.029268	80.390244

	compactness	circularity	distance_circularity	radius_ratio	pr.axis_aspect_ratio	max.length_aspect_ratio	scatter_ratio	elongatedness	pr.axis_rectangularity	max.length_rectangularity	scaled_variance	scaled_variance.1	scaled_radius_of_gyration	scaled_radius_of_gyration.1	skewness_about	skewness_about.1	skewness_about.2	hollows_ratio
compactness	1.000000	0.689885	0.789955	0.688130	0.090557	0.150369	0.814026	-0.788051	0.814227	0.674902	0.764386	0.820240	0.581405	-0.258437	0.231648	0.168384	0.296195	0.372806
circularity	0.689885	1.000000	0.797704	0.623950	0.155023	0.251619	0.858149	-0.825108	0.856137	0.965366	0.806108	0.850932	0.935594	0.049070	0.141726	-0.001975	-0.113902	0.049331
distance_circularity	0.789955	0.797704	1.000000	0.771404	0.163386	0.265591	0.909023	-0.912713	0.897261	0.773459	0.865683	0.891789	0.705689	-0.238145	0.110280	0.277851	0.145258	0.343228
radius_ratio	0.688130	0.623950	0.771404	1.000000	0.667375	0.452460	0.743470	-0.795761	0.716210	0.570478	0.806788	0.731773	0.544636	-0.175348	0.044693	0.178079	0.375591	0.470895
pr.axis_aspect_ratio	0.090557	0.155023	0.163386	0.667375	1.000000	0.652093	0.113696	-0.191193	0.086992	0.133553	0.290375	0.100668	0.135663	0.173060	-0.059244	-0.040769	0.229702	0.257566
max.length_aspect_ratio	0.150369	0.251619	0.265591	0.452460	0.652093	1.000000	0.171445	-0.183242	0.167514	0.309180	0.331124	0.150069	0.197179	0.308329	0.016461	0.041210	-0.030543	0.139283
scatter_ratio	0.814026	0.858149	0.909023	0.743470	0.113696	0.171445	1.000000	-0.973413	0.991992	0.808154	0.950067	0.996396	0.795748	-0.045632	0.070118	0.227375	0.009967	0.138424
elongatedness	-0.788051	-0.825108	-0.912713	-0.795761	-0.191193	-0.183242	-0.973413	1.000000	-0.950345	-0.771099	-0.937846	-0.956858	-0.761563	0.119750	-0.046621	-0.201325	-0.117568	-0.233408
pr.axis_rectangularity	0.814227	0.856137	0.897261	0.716210	0.086992	0.167514	0.991992	-0.950345	1.000000	0.811979	0.935653	0.992119	0.792895	-0.033769	0.078701	0.231171	-0.017383	0.117202
max.length_rectangularity	0.674902	0.965366	0.773459	0.570478	0.133553	0.309180	0.808154	-0.771099	0.811979	1.000000	0.744760	0.796230	0.865240	0.031098	0.130356	0.013045	-0.108019	0.086792
scaled_variance	0.764386	0.806108	0.865683	0.806788	0.290375	0.331124	0.950067	-0.937846	0.935653	0.744760	1.000000	0.947617	0.776051	0.099371	0.034228	0.208966	0.017828	0.104989
scaled_variance.1	0.820240	0.850932	0.891789	0.731773	0.100668	0.150069	0.996396	-0.956858	0.992119	0.796230	0.947617	1.000000	0.791997	-0.037903	0.072393	0.220054	0.011782	0.125118
scaled_radius_of_gyration	0.581405	0.935594	0.705689	0.544636	0.135663	0.197179	0.795748	-0.761563	0.792895	0.865240	0.776051	0.791997	1.000000	0.177284	0.162397	-0.041153	-0.224495	-0.102167
scaled_radius_of_gyration.1	-0.258437	0.049070	-0.238145	-0.175348	0.173060	0.308329	-0.045632	0.119750	-0.033769	0.031098	0.099371	-0.037903	0.177284	1.000000	-0.088109	-0.120600	-0.748668	-0.798810
skewness_about	0.231648	0.141726	0.110280	0.044693	-0.059244	0.016461	0.070118	-0.046621	0.078701	0.130356	0.034228	0.072393	0.162397	-0.088109	1.000000	-0.022611	0.111135	0.098128
skewness_about.1	0.168384	-0.001975	0.277851	0.178079	-0.040769	0.041210	0.227375	-0.201325	0.231171	0.013045	0.208966	0.220054	-0.041153	-0.120600	-0.022611	1.000000	0.077942	0.201286
skewness_about.2	0.296195	-0.113902	0.145258	0.375591	0.229702	-0.030543	0.009967	-0.117568	-0.017383	-0.108019	0.017828	0.011782	-0.224495	-0.748668	0.111135	0.077942	1.000000	0.894057
hollows_ratio	0.372806	0.049331	0.343228	0.470895	0.257566	0.139283	0.138424	-0.233408	0.117202	0.086792	0.104989	0.125118	-0.102167	-0.798810	0.098128	0.201286	0.894057	1.000000

	Runs	Ave	SR	Fours	Sixes	HF
Runs	1.000000	0.692984	0.493489	0.918809	0.769778	0.835148
Ave	0.692984	1.000000	0.623606	0.546211	0.682414	0.620754
SR	0.493489	0.623606	1.000000	0.384810	0.583943	0.427584
Fours	0.918809	0.546211	0.384810	1.000000	0.522574	0.783689
Sixes	0.769778	0.682414	0.583943	0.522574	1.000000	0.767696
HF	0.835148	0.620754	0.427584	0.783689	0.767696	1.000000

	Name	Runs	Ave	SR	Fours	Sixes	HF	GROUP
1	CH Gayle	733.0	61.08	160.74	46.0	59.0	9.0	1
3	G Gambhir	590.0	36.87	143.55	64.0	17.0	6.0	1
5	V Sehwag	495.0	33.00	161.23	57.0	19.0	5.0	1
7	CL White	479.0	43.54	149.68	41.0	20.0	5.0	1
9	S Dhawan	569.0	40.64	129.61	58.0	18.0	5.0	1

	count	unique	top	freq	mean	std	min	25%	50%	75%	max
compactness	813.0	NaN	NaN	NaN	93.656827	8.233751	73.0	87.0	93.0	100.0	119.0
circularity	813.0	NaN	NaN	NaN	44.803198	6.146659	33.0	40.0	44.0	49.0	59.0
distance_circularity	813.0	NaN	NaN	NaN	82.04305	15.78307	40.0	70.0	79.0	98.0	112.0
radius_ratio	813.0	NaN	NaN	NaN	169.098401	33.615402	104.0	141.0	167.0	195.0	333.0
pr.axis_aspect_ratio	813.0	NaN	NaN	NaN	61.774908	7.973	47.0	57.0	61.0	65.0	138.0
max.length_aspect_ratio	813.0	NaN	NaN	NaN	8.599016	4.677174	2.0	7.0	8.0	10.0	55.0
scatter_ratio	813.0	NaN	NaN	NaN	168.563346	33.082186	112.0	146.0	157.0	198.0	265.0
elongatedness	813.0	NaN	NaN	NaN	40.98893	7.80338	26.0	33.0	43.0	46.0	61.0
pr.axis_rectangularity	813.0	NaN	NaN	NaN	20.558426	2.573184	17.0	19.0	20.0	23.0	29.0
max.length_rectangularity	813.0	NaN	NaN	NaN	147.891759	14.504648	118.0	137.0	146.0	159.0	188.0
scaled_variance	813.0	NaN	NaN	NaN	188.377614	31.165873	130.0	167.0	179.0	217.0	320.0
scaled_variance.1	813.0	NaN	NaN	NaN	438.382534	175.270368	184.0	318.0	364.0	586.0	1018.0
scaled_radius_of_gyration	813.0	NaN	NaN	NaN	174.252153	32.332161	109.0	149.0	173.0	198.0	268.0
scaled_radius_of_gyration.1	813.0	NaN	NaN	NaN	72.399754	7.475994	59.0	67.0	71.0	75.0	135.0
skewness_about	813.0	NaN	NaN	NaN	6.351784	4.921476	0.0	2.0	6.0	9.0	22.0
skewness_about.1	813.0	NaN	NaN	NaN	12.687577	8.926951	0.0	6.0	11.0	19.0	41.0
skewness_about.2	813.0	NaN	NaN	NaN	188.97909	6.153681	176.0	184.0	189.0	193.0	206.0
hollows_ratio	813.0	NaN	NaN	NaN	195.729397	7.398781	181.0	191.0	197.0	201.0	211.0
class	813	3	car	413	NaN	NaN	NaN	NaN	NaN	NaN	NaN

	Model	Accuracy Score of Test Data
0	Support Vector Classifier without PCA	95.491803
1	Support Vector Classifier with PCA	82.377049

	count	unique	top	freq	mean	std	min	25%	50%	75%	max
Name	90	90	R Vinay Kumar	1	NaN	NaN	NaN	NaN	NaN	NaN	NaN
Runs	90.0	NaN	NaN	NaN	219.933333	156.253669	2.0	98.0	196.5	330.75	733.0
Ave	90.0	NaN	NaN	NaN	24.729889	13.619215	0.5	14.665	24.44	32.195	81.33
SR	90.0	NaN	NaN	NaN	119.164111	23.656547	18.18	108.745	120.135	131.9975	164.1
Fours	90.0	NaN	NaN	NaN	19.788889	16.399845	0.0	6.25	16.0	28.0	73.0
Sixes	90.0	NaN	NaN	NaN	7.577778	8.001373	0.0	3.0	6.0	10.0	59.0
HF	90.0	NaN	NaN	NaN	1.188889	1.688656	0.0	0.0	0.5	2.0	9.0

	Runs	Ave	SR	Fours	Sixes	HF
GROUP
0	0.633139	0.668164	0.429559	0.564805	0.445810	0.371363
1	2.132486	1.191316	1.011647	2.097750	1.938245	2.694894
2	-0.589754	-0.432593	-0.091036	-0.562606	-0.483927	-0.586181
3	-1.319810	-1.526625	-2.403115	-1.143333	-0.934411	-0.707989

	Name	Runs	Ave	SR	Fours	Sixes	HF	GROUP
13	KP Pietersen	305.0	61.00	147.34	22.0	20.0	3.0	Grade A
17	AB de Villiers	319.0	39.87	161.11	26.0	15.0	3.0	Grade A
19	JP Duminy	244.0	81.33	128.42	13.0	11.0	2.0	Grade A
21	DA Warner	256.0	36.57	164.10	28.0	14.0	3.0	Grade A
23	SR Watson	255.0	42.50	151.78	26.0	14.0	2.0	Grade A
25	F du Plessis	398.0	33.16	130.92	29.0	17.0	3.0	Grade A
27	OA Shah	340.0	37.77	132.81	24.0	16.0	3.0	Grade A
29	DJ Bravo	371.0	46.37	140.53	20.0	20.0	0.0	Grade A
31	DJ Hussey	396.0	33.00	129.83	28.0	17.0	2.0	Grade A
33	SK Raina	441.0	25.94	135.69	36.0	19.0	1.0	Grade A
35	AT Rayudu	333.0	37.00	132.14	21.0	14.0	2.0	Grade A
37	Mandeep Singh	432.0	27.00	126.31	53.0	7.0	2.0	Grade A
39	R Dravid	462.0	28.87	112.13	63.0	4.0	2.0	Grade A
41	DR Smith	157.0	39.25	160.20	18.0	7.0	1.0	Grade A
43	M Vijay	336.0	25.84	125.84	39.0	10.0	2.0	Grade A
45	SPD Smith	362.0	40.22	135.58	24.0	14.0	0.0	Grade A
47	TM Dilshan	285.0	35.62	109.19	33.0	5.0	3.0	Grade A
49	RV Uthappa	405.0	27.00	118.07	38.0	10.0	2.0	Grade A
51	SE Marsh	336.0	30.54	120.00	39.0	7.0	2.0	Grade A
53	KA Pollard	220.0	24.44	138.36	15.0	14.0	2.0	Grade A
55	DMD Jayawardene	335.0	27.91	112.41	39.0	3.0	3.0	Grade A
57	V Kohli	364.0	28.00	111.65	33.0	9.0	2.0	Grade A
59	MA Agarwal	225.0	20.45	142.40	19.0	15.0	1.0	Grade A
61	SR Tendulkar	324.0	29.45	114.48	39.0	4.0	2.0	Grade A
63	MEK Hussey	261.0	32.62	110.59	28.0	8.0	2.0	Grade A
65	JH Kallis	409.0	25.56	106.51	34.0	10.0	2.0	Grade A
67	MS Dhoni	357.0	29.75	128.41	26.0	9.0	1.0	Grade A
69	MS Bisla	213.0	30.42	133.12	16.0	10.0	1.0	Grade A
71	JD Ryder	256.0	25.60	120.75	23.0	8.0	2.0	Grade A
73	BJ Hodge	245.0	30.62	140.00	18.0	9.0	0.0	Grade A
75	NV Ojha	255.0	23.18	113.83	21.0	13.0	1.0	Grade A
81	BB McCullum	289.0	24.08	102.12	37.0	3.0	1.0	Grade A

	Name	Runs	Ave	SR	Fours	Sixes	HF	GROUP
77	DB Das	126.0	42.00	135.48	9.0	6.0	0.0	Grade C
79	AC Gilchrist	172.0	34.40	120.27	21.0	4.0	1.0	Grade C
83	IK Pathan	176.0	25.14	139.68	14.0	6.0	0.0	Grade C
85	Azhar Mahmood	186.0	23.25	130.98	16.0	8.0	0.0	Grade C
87	MK Pandey	143.0	20.42	127.67	12.0	6.0	1.0	Grade C
89	S Badrinath	196.0	28.00	108.28	23.0	2.0	1.0	Grade C
91	DA Miller	98.0	32.66	130.66	6.0	4.0	0.0	Grade C
93	MK Tiwary	260.0	26.00	105.69	21.0	3.0	1.0	Grade C
95	JA Morkel	107.0	15.28	157.35	5.0	6.0	0.0	Grade C
97	LRPL Taylor	197.0	19.70	115.20	12.0	7.0	1.0	Grade C
99	M Manhas	120.0	30.00	125.00	10.0	4.0	0.0	Grade C
101	DT Christian	145.0	29.00	122.88	8.0	6.0	0.0	Grade C
103	RA Jadeja	191.0	15.91	126.49	13.0	9.0	0.0	Grade C
105	JEC Franklin	220.0	24.44	98.65	15.0	6.0	1.0	Grade C
107	KC Sangakkara	200.0	18.18	108.69	21.0	4.0	1.0	Grade C
109	Y Nagar	153.0	30.60	115.03	13.0	3.0	0.0	Grade C
111	STR Binny	90.0	22.50	134.32	9.0	3.0	0.0	Grade C
113	SS Tiwary	191.0	23.87	112.35	9.0	8.0	0.0	Grade C
115	KD Karthik	238.0	18.30	111.73	30.0	2.0	0.0	Grade C
117	AL Menaria	220.0	20.00	108.91	14.0	8.0	0.0	Grade C
119	PA Patel	194.0	17.63	117.57	19.0	4.0	0.0	Grade C
121	SC Ganguly	268.0	17.86	98.89	30.0	4.0	0.0	Grade C
123	YK Pathan	194.0	19.40	114.79	10.0	7.0	0.0	Grade C
125	Harbhajan Singh	108.0	12.00	135.00	14.0	3.0	0.0	Grade C
127	RE Levi	83.0	13.83	113.69	10.0	4.0	1.0	Grade C
129	LR Shukla	75.0	12.50	131.57	4.0	5.0	0.0	Grade C
131	Y Venugopal Rao	132.0	22.00	104.76	8.0	5.0	0.0	Grade C
133	AD Mathews	127.0	18.14	117.59	5.0	4.0	0.0	Grade C
135	PP Chawla	106.0	13.25	120.45	9.0	4.0	0.0	Grade C
137	Shakib Al Hasan	91.0	15.16	122.97	6.0	3.0	0.0	Grade C
139	N Saini	140.0	14.00	99.29	16.0	0.0	1.0	Grade C
141	MN Samuels	124.0	17.71	100.81	7.0	5.0	0.0	Grade C
143	MJ Clarke	98.0	16.33	104.25	12.0	0.0	0.0	Grade C
145	R Bhatia	35.0	11.66	125.00	4.0	0.0	0.0	Grade C
147	R Vinay Kumar	68.0	13.60	109.67	3.0	2.0	0.0	Grade C
149	P Kumar	35.0	11.66	116.66	2.0	1.0	0.0	Grade C
151	J Botha	58.0	14.50	107.40	4.0	1.0	0.0	Grade C
153	A Ashish Reddy	35.0	8.75	120.68	3.0	1.0	0.0	Grade C
155	DL Vettori	31.0	7.75	119.23	3.0	1.0	0.0	Grade C
157	SP Goswami	69.0	13.80	102.98	4.0	1.0	0.0	Grade C
159	SL Malinga	55.0	9.16	103.77	4.0	3.0	0.0	Grade C
161	RJ Peterson	32.0	10.66	106.66	3.0	1.0	0.0	Grade C
163	R Ashwin	18.0	6.00	120.00	2.0	0.0	0.0	Grade C
165	B Kumar	40.0	13.33	100.00	4.0	0.0	0.0	Grade C

	Name	Runs	Ave	SR	Fours	Sixes	GROUP
167	DW Steyn	19.0	3.80	90.47	0.0	1.0	Grade D
169	A Mishra	16.0	5.33	80.00	1.0	0.0	Grade D
171	Z Khan	12.0	6.00	70.58	1.0	0.0	Grade D
173	WD Parnell	19.0	4.75	70.37	2.0	0.0	Grade D
175	PC Valthaty	30.0	5.00	58.82	4.0	0.0	Grade D
177	RP Singh	6.0	3.00	50.00	0.0	0.0	Grade D
179	R Sharma	2.0	0.50	18.18	0.0	0.0	Grade D

	Model	Accuracy Score of Test Data
0	GuassianNB without Dimensionality Reduction	83.148148
1	GuassianNB with Dimensionality Reduction	72.222222

Part ONE - Project Based¶

Importing the libraries¶

Import and warehouse data¶

Observations:¶

Data Cleansing¶

Observations:¶

Data Analysis and Visualisation¶

Observations:¶

Observations:¶

Machine Learning¶

K-Means¶

Observations:¶

Hierarchical Clustering¶

Observations:¶

Observations:¶

Linear Regression on data set without clusters¶

Linear Regression on data set with K-Means clusters¶

Obseravtions:¶

Improvisations:¶

---------------------------------------------------------------------------------------------------------------¶

Part Two- Project Based¶

Importing the necessary libraries¶

Importing the data¶

Observations:¶

Analysis and Visualisation¶

Observations:¶

K-Means Clustering¶

Observations:¶

---------------------------------------------------------------------------------------------------------------¶

Part Three - Project Based¶

Importing the libraries¶

Import data¶

Data Analysis and Visualisation¶

Observations:¶

Observations:¶

Observations:¶

PCA and Machine Learning Models¶

SVM with all attributes¶

PCA¶

Observations:¶

Dimensionality Reduction¶

SVM with only transformed attributes¶

Observations:¶

---------------------------------------------------------------------------------------------------------------¶

Part Four - Project Based¶

Importing the libraries¶

Data Import¶

Data Analysis and Visualisation¶

Observations:¶

Observations:¶

Observations:¶

Machine Learning model¶

PCA¶

Observations:¶

Dimensionality Reduction¶

Observations:¶

K-Means Clustering with transformed variables¶

Observations:¶

Conclusions:¶

---------------------------------------------------------------------------------------------------------------¶

Part Five - Question Based¶

Dimensionality Reduction Techniques in Python¶

Feature Selection:¶

Component/Factor Based:¶

Projection Based:¶

Using Dimensionality Reduction in Multi-Media(images,videos)¶

Importing the libraries¶

Observations:¶

GuassianNB with original data¶

Dimensionality reduction using Projection Based methods¶

GuassianNB model with reduced features¶

Observations:¶